Platform Engineer III

Job Summary

As the Data & AI Lead Engineer, you will bridge the gap between complex data engineering and cutting-edge artificial intelligence. You will architect, build, and scale robust data pipelines while leading the integration of Machine Learning (ML) and Large Language Model (LLM) workflows into our core products

Key Responsibilities

Architecture & Core Engineering

• Data & AI Infrastructure: Design, implement, and maintain scalable, fault-tolerant data pipelines (ETL/ELT) and MLOps frameworks to support real-time and batch processing.

• AI Integration: Architect and deploy production-grade ML models, Generative AI applications, and Retrieval-Augmented Generation (RAG) systems.

• Data Modeling: Establish best practices for data warehousing, lakehouses, and vector databases to support both analytical and operational AI needs.

• Performance Tuning: Optimize distributed computing workloads and model inference costs for high throughput and low latency.

2. Leadership & Strategy

• Team Mentorship: Guide, code-review, and mentor a team of data and ML engineers, fostering a culture of technical excellence and continuous learning.

• AI Roadmap: Partner with leadership to define the company’s AI capabilities, evaluating new frameworks, tools, and SaaS providers.

• Governance & Compliance: Define data governance, security policies, and ethical AI guidelines (e.g., data privacy, bias mitigation, model drift tracking).

Skill Requirements

• Programming: Mastery of** Python** and solid proficiency in** SQL**,Scala, or Java.

• Data Engineering: Deep experience with modern data stack tools like** Spark**,Databricks,Snowflake,dbt, and orchestrators like Apache Airflow.

• AI & Machine Learning: Practical experience deploying frameworks like** Py Torch** or** Tensor Flow**, alongside GenAI tools like Lang Chain,Llama Index, and vector databases (e.g.,Pinecone,Milvus, or Chroma).

Cloud & DevOps: Strong hands-on experience with cloud platforms (AWS/Azure/GCP) and containerization (Docker,Kubernetes), specifically tailored for MLOps

Other Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field.

• 6+ years of professional experience in data engineering or software engineering.

• 2+ years of experience leading technical teams or acting as a principal/lead engineer.

• Proven track record of taking AI/ML models out of notebooks and successfully embedding them into production-grade software.